Data-driven optimization of maskless grayscale laser lithography.

ABSTRACT: A data-driven optimization algorithm is described for maskless grayscale laser lithography (MGLL), providing effective correction for topography errors deriving from the non-linear response of the photoresist to exposure dose, proximity effects and sharp dose transitions near edges and vertices. The algorithm employs an artificial neural network (ANN) to represent the physical process, with the MGLL virtual photomask and its radial averages as input variables and the predicted surface topography as the output. Virtual photomask optimization is achieved by predicting the surface topography obtained with a given virtual photomask and implementing corrections based on the predicted error. An elaborate training model with a range of different surface features is employed to acquire a comprehen

SUBMITTER: Lutey AHA 

PROVIDER: S-EPMC12623498 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature